Automated Depth-Level Classification of Impacted Mandibular Third Molars Using Deep Learning on Panoramic X-Rays

The extraction of impacted mandibular third molars are common but complex procedure in oral surgery, often assessed using panoramic radiographs. This study proposes a deep learning-based approach for the automatic segmentation and depth-level classification of mandibular third molar teeth according to the Pell & Gregory classification method. Utilizing the YOLOv7 segmentation model, we segmented the molar teeth and mandible and developed an algorithm for depth-level classification. Despite the small training dataset of 315 images, our models achieved high performance, with Dice Similarity Coefficients (DSC) ranging from 0.9001 to 0.9120 for teeth segmentation and 0.9708 for mandible segmentation. The depth-level classification accuracy was 0.7794 for T32 teeth and 0.9118 for T17 teeth. The proposed method demonstrates the potential of deep learning in automating the assessment of third molar depth-level extraction difficulty, offering a scalable and accurate solution for clinical use.